Decentralized AI-Control Framework for Multi-Party Multi-Network 6G Deployments
Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will necessarily have to encompass both the radio and the fiber-optical domain. This paper introduces the Decentralized Multi-Party, Multi-Network AI (DMMAI) framework for integrating AI into 6G networks deployed at scale. DMMAI harmonizes AI-driven controls across diverse network platforms and thus facilitates networks that autonomously configure, monitor, and repair themselves. This is particularly crucial at the network edge, where advanced applications meet heightened functionality and security demands. The radio/optical integration is vital due to the current compartmentalization of AI research within these domains, which lacks a comprehensive understanding of their interaction. Our approach explores multi-network orchestration and AI control integration, filling a critical gap in standardized frameworks for AI-driven coordination in 6G networks. The DMMAI framework is a step towards a global standard for AI in 6G, aiming to establish reference use cases, data and model management methods, and benchmarking platforms for future AI/ML solutions.
Authors
- Christian Thomsen (ORCID: https://orcid.org/0000-0002-0862-0509)
- Christos Tranoris (ORCID: https://orcid.org/0000-0002-3433-037X)
- Thomas Kyriakakis (ORCID: https://orcid.org/0000-0002-0654-1452)
- Johann M. Márquez-Barja (ORCID: https://orcid.org/0000-0001-5660-3597)
- Jinfeng Du (ORCID: https://orcid.org/0000-0002-9709-0713)
- Merim Dzaferagic (ORCID: https://orcid.org/0000-0003-1254-4163)
- Seshu Tirupathi (ORCID: https://orcid.org/0000-0003-2998-0826)
- Torben Pedersen (ORCID: https://orcid.org/0000-0001-7541-9365)
- Søren Kejser Jensen (ORCID: https://orcid.org/0000-0003-1184-1000)
- Shashi Raj Pandey (ORCID: https://orcid.org/0000-0002-5781-4131)
- Spyros Denazis (ORCID: https://orcid.org/0000-0001-6771-3038)
- Tingjun Chen (ORCID: https://orcid.org/0000-0002-5717-5755)
- Daniel C. Kilper (ORCID: https://orcid.org/0000-0003-3542-5335)
- Luiz A. DaSilva (ORCID: https://orcid.org/0000-0001-6310-6150)
- Holger Claußen (ORCID: https://orcid.org/0000-0003-2045-2082)
- Panagiotis A. Karafotis (ORCID: https://orcid.org/0000-0002-7556-4666)
- Ivan Seskar (ORCID: https://orcid.org/0000-0002-7977-0176)
- Junya Shiraishi (ORCID: https://orcid.org/0000-0003-0268-9297)
- Yiran Chen (ORCID: https://orcid.org/0000-0002-1486-8412)
- Joao F. Santos (ORCID: https://orcid.org/0000-0001-6439-2056)
- Petar Popovski (ORCID: https://orcid.org/0000-0001-6195-4797)
- Nina Slamnik–Kriještorac (ORCID: https://orcid.org/0000-0003-1719-772X)
- Georgios Christos Tziavas
- Gil Zussman
- Marco Ruffini
Institutions
- Rutgers, The State University of New Jersey (US)
- University of Antwerp (BE)
- Duke University (US)
- University of Patras (GR)
- Trinity College Dublin (IE)
- Duke Energy (United States) (US)
- D-Tech (United States) (US)
- Nokia (United States) (US)
- Columbia University (US)
- Virginia Tech (US)
- Aalborg University (DK)
Publication Details
- Published
- 2025-06-08
- DOI
- https://doi.org/10.1109/iccworkshops67674.2025.11162408
- Primary Topic
- Software-Defined Networks and 5G
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- HORIZON EUROPE Framework Programme